{"60811":{"#nid":"60811","#data":{"type":"event","title":"Health Systems Seminar Series with Dr. Julie Simmons Ivy","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E\u0026nbsp; When to\nRespond: A Multi-Agent Stochastic Alert Threshold Model for Declaring a Disease\nOutbreak\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u0026nbsp;\u003C\/strong\u003E\u0026nbsp;Julie Simmons Ivy, Associate Professor, Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EInfluenza pandemics are considered\none of the most significant and widely spread threats to public health. In this\nresearch, we explore the relationship between local and state health\ndepartments with respect to issuing alerts and responding to a potential\ndisease outbreak such as influenza. We modeled the public health system as a\nmulti-agent (or decentralized) partially observable Markov decision process\nwhere local and state health departments are decision makers. The model is used\nto determine when local and state decision makers should issue an alert or\ninitiate mitigation actions such as vaccination in response to the existence of\na disease threat. The model incorporates the fact that health departments have\nimperfect information about the exact number of infected people. The objective\nof the model is to minimize both false alerts and late alerts while identifying\nthe optimal timing for alerting decisions. Providing such a balance between\nfalse and late alerts has the potential to increase the credibility and\nefficiency of the public health system while improving immediate response and\ncare in the event of a public health emergency. Using data from the 2009-2010\nH1N1 influenza outbreak to estimate model parameters including observations and\ntransition probabilities, computational results for near optimal solutions are\nobtained.\u0026nbsp; In order to gain insight regarding the structure of optimal\npolicies at the local and state levels, various model parameters including\nfalse and late alerting costs are explored.\u0026nbsp; \u003C\/p\u003E\n\n\n\n\u003Cp\u003EThis research is a part of the North\nCarolina Preparedness and Emergency Response Research Center (NCPERRC) and was\nsupported by the Centers for Disease Control and Prevention (CDC) Grant 1PO1 TP\n000296-02.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EGeorgia Tech\u0027s Health Systems Institute welcomes Dr. Julie Simmons Ivy, on \u0022When to Respond: A Multi-Agent Stochastic Alert Threshold Model for Declaring a Disease Outbreak.\u0022\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"When to Respond: A Multi-Agent Stochastic Alert Threshold Model for Declaring a Disease Outbreak"}],"uid":"27187","created_gmt":"2010-09-07 09:59:57","changed_gmt":"2016-10-08 01:52:15","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-10-06T12:00:00-04:00","event_time_end":"2010-10-06T13:00:00-04:00","event_time_end_last":"2010-10-06T13:00:00-04:00","gmt_time_start":"2010-10-06 16:00:00","gmt_time_end":"2010-10-06 17:00:00","gmt_time_end_last":"2010-10-06 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"related_links":[{"url":"http:\/\/www.ise.ncsu.edu\/people\/faculty\/ivy.php","title":"Dr. Julie Simmons Ivy"},{"url":"http:\/\/www.hsi.gatech.edu\/","title":"Health Systems Institute at Georgia Tech and Emory University"}],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"7896","name":"crisis"},{"id":"5302","name":"Disease"},{"id":"550","name":"health systems"},{"id":"755","name":"public health"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}